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Abstract

We present Gibbs Markov Random Field models as a powerful and robust descriptor of spatial information in typical remote sensing image data. This class of stochastic image models provides an intuitive description of the image data using parameters of an energy function. For the selection among several nested models and the fit of the model we proceed in two steps of Bayesian inference. This procedure yields the most plausible model and its most likely parameters which together describe the image content in an optimal way. Its additional application at multiple scales of the image enables us to capture all structures being present in complex remote sensing images. The calculation of the evidences of various models applied to the resulting quasi-continuous image pyramid automatically detects such structures. We present examples for both Synthetic Aperture Radar (SAR) and optical data.